ArticleJournal of clinical laboratory analysis2025
Quantitative Analysis of DNA Double-Strand Breaks in Genomic DNA Using Standard Curve Method.
Article in Journal of clinical laboratory analysis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundDNA double-strand breaks (DSBs) are the most lethal and dangerous type of lesions with significant implications for both cellular function and organismal health. The number of DSBs (N
methodGenomic DNA from human, mouse, Arabidopsis, Saccharomyces cerevisiae, and Escherichia coli was digested by seven blunt-end restriction enzymes to generate DSB standards. Theoretical N
resultsAll genomes were successfully digested by seven blunt-end restriction enzymes to produce standard DSB fragments. Standard curves demonstrated high linearity (R
conclusionThis standard curve-based method enables accurate, reproducible quantification of genome-wide DSBs in various organisms. It is simple, low-cost, and easily standardized, offering a promising tool for applications in genotoxicity testing, environmental exposure monitoring, and DNA damage research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.